import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


A						=			datasets.utils.logging.get_logger(__name__)





@dataclass
class     lowerCAmelCase__     (				datasets.BuilderConfig			):


		'''simple docstring'''






		lowerCAmelCase_     =   None
		lowerCAmelCase_     =   "utf-8"
		lowerCAmelCase_     =   None
		lowerCAmelCase_     =   None
		lowerCAmelCase_     =   True  # deprecated
		lowerCAmelCase_     =   None  # deprecated
		lowerCAmelCase_     =   10 << 20  # 10MB
		lowerCAmelCase_     =   None





class     lowerCAmelCase__     (				datasets.ArrowBasedBuilder			):


		'''simple docstring'''






		lowerCAmelCase_     =   JsonConfig
		def   _snake_case       (       self     :     Optional[Any]		)      ->	int:
				if self.config.block_size is not None:
						logger.warning('The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'		)
						_lowerCamelCase				   =  self.config.block_size
				if self.config.use_threads is not True:
						logger.warning(
						    'The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'		)
				if self.config.newlines_in_values is not None:
						raise ValueError('The JSON loader parameter `newlines_in_values` is no longer supported'		)
				return datasets.DatasetInfo(features=self.config.features		)
		def   _snake_case       (       self     :     Tuple     ,  snake_case__     :     Union[str, Any]		)      ->	List[str]:
				if not self.config.data_files:
						raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""		)
				_lowerCamelCase				   =  dl_manager.download_and_extract(self.config.data_files		)
				if isinstance(snake_case__     ,  (str, list, tuple)		):
						_lowerCamelCase				   =  data_files
						if isinstance(snake_case__     ,  snake_case__		):
								_lowerCamelCase				   =  [files]
						_lowerCamelCase				   =  [dl_manager.iter_files(snake_case__		) for file in files]
						return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,  gen_kwargs={'files': files}		)]
				_lowerCamelCase				   =  []
				for split_name, files in data_files.items():
						if isinstance(snake_case__     ,  snake_case__		):
								_lowerCamelCase				   =  [files]
						_lowerCamelCase				   =  [dl_manager.iter_files(snake_case__		) for file in files]
						splits.append(datasets.SplitGenerator(name=snake_case__     ,  gen_kwargs={'files': files}		)		)
				return splits
		def   _snake_case       (       self     :     Tuple     ,  snake_case__     :     pa.Table		)      ->	pa.Table:
				if self.config.features is not None:
						# adding missing columns
						for column_name in set(self.config.features		) - set(pa_table.column_names		):
								_lowerCamelCase				   =  self.config.features.arrow_schema.field(snake_case__		).type
								_lowerCamelCase				   =  pa_table.append_column(snake_case__     ,  pa.array([None] * len(snake_case__		)     ,  type=snake_case__		)		)
						# more expensive cast to support nested structures with keys in a different order
						# allows str <-> int/float or str to Audio for example
						_lowerCamelCase				   =  table_cast(snake_case__     ,  self.config.features.arrow_schema		)
				return pa_table



		def   _snake_case       (       self     :     Any     ,  snake_case__     :     Optional[int]		)      ->	Union[str, Any]:
				for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case__		)		):
						# If the file is one json object and if we need to look at the list of items in one specific field
						if self.config.field is not None:
								with open(snake_case__     ,  encoding=self.config.encoding     ,  errors=self.config.encoding_errors		) as f:
										_lowerCamelCase				   =  json.load(snake_case__		)

								# We keep only the field we are interested in
								_lowerCamelCase				   =  dataset[self.config.field]

								# We accept two format: a list of dicts or a dict of lists
								if isinstance(snake_case__     ,  (list, tuple)		):
										_lowerCamelCase				   =  set().union(*[row.keys() for row in dataset]		)
										_lowerCamelCase				   =  {col: [row.get(snake_case__		) for row in dataset] for col in keys}
								else:
										_lowerCamelCase				   =  dataset
								_lowerCamelCase				   =  pa.Table.from_pydict(snake_case__		)
								yield file_idx, self._cast_table(snake_case__		)

						# If the file has one json object per line
						else:
								with open(snake_case__     ,  'rb'		) as f:
										_lowerCamelCase				   =  0
										# Use block_size equal to the chunk size divided by 32 to leverage multithreading
										# Set a default minimum value of 16kB if the chunk size is really small
										_lowerCamelCase				   =  max(self.config.chunksize // 3_2     ,  1_6 << 1_0		)
										_lowerCamelCase				   =  (
										    self.config.encoding_errors if self.config.encoding_errors is not None else 'strict'
										)
										while True:
												_lowerCamelCase				   =  f.read(self.config.chunksize		)
												if not batch:
														break
												# Finish current line
												try:
														batch += f.readline()
												except (AttributeError, io.UnsupportedOperation):
														batch += readline(snake_case__		)
												# PyArrow only accepts utf-8 encoded bytes
												if self.config.encoding != "utf-8":
														_lowerCamelCase				   =  batch.decode(self.config.encoding     ,  errors=snake_case__		).encode('utf-8'		)
												try:
														while True:
																try:
																		_lowerCamelCase				   =  paj.read_json(
																		    io.BytesIO(snake_case__		)     ,  read_options=paj.ReadOptions(block_size=snake_case__		)		)
																		break
																except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
																		if (
																		    isinstance(snake_case__     ,  pa.ArrowInvalid		)
																		    and "straddling" not in str(snake_case__		)
																		    or block_size > len(snake_case__		)
																		):
																				raise
																		else:
																				# Increase the block size in case it was too small.
																				# The block size will be reset for the next file.
																				logger.debug(
																				    f"""Batch of {len(snake_case__		)} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."""		)
																				block_size *= 2
												except pa.ArrowInvalid as e:
														try:
																with open(
																    snake_case__     ,  encoding=self.config.encoding     ,  errors=self.config.encoding_errors		) as f:
																		_lowerCamelCase				   =  json.load(snake_case__		)
														except json.JSONDecodeError:
																logger.error(f"""Failed to read file '{file}' with error {type(snake_case__		)}: {e}"""		)
																raise e
														# If possible, parse the file as a list of json objects and exit the loop
														if isinstance(snake_case__     ,  snake_case__		):  # list is the only sequence type supported in JSON
																try:
																		_lowerCamelCase				   =  set().union(*[row.keys() for row in dataset]		)
																		_lowerCamelCase				   =  {col: [row.get(snake_case__		) for row in dataset] for col in keys}
																		_lowerCamelCase				   =  pa.Table.from_pydict(snake_case__		)
																except (pa.ArrowInvalid, AttributeError) as e:
																		logger.error(f"""Failed to read file '{file}' with error {type(snake_case__		)}: {e}"""		)
																		raise ValueError(f"""Not able to read records in the JSON file at {file}."""		) from None
																yield file_idx, self._cast_table(snake_case__		)
																break
														else:
																logger.error(f"""Failed to read file '{file}' with error {type(snake_case__		)}: {e}"""		)
																raise ValueError(
																    f"""Not able to read records in the JSON file at {file}. """
																    f"""You should probably indicate the field of the JSON file containing your records. """
																    f"""This JSON file contain the following fields: {str(list(dataset.keys()		)		)}. """
																    f"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """		) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
												yield (file_idx, batch_idx), self._cast_table(snake_case__		)
												batch_idx += 1
from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    float:
		return math.sqrt(sum(pow(a - b			,       2							) for a, b in zip(UpperCamelCase			,       UpperCamelCase							)							)							)


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    list[list[list[float] | float]]:

		if dataset.ndim != value_array.ndim:
				_lowerCamelCase				   =  (
				    'Wrong input data\'s dimensions... '
				    F"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
				)
				raise ValueError(UpperCamelCase							)

		try:
				if dataset.shape[1] != value_array.shape[1]:
						_lowerCamelCase				   =  (
						    'Wrong input data\'s shape... '
						    F"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
						)
						raise ValueError(UpperCamelCase							)
		except IndexError:
				if dataset.ndim != value_array.ndim:
						raise TypeError('Wrong shape'							)

		if dataset.dtype != value_array.dtype:
				_lowerCamelCase				   =  (
				    'Input data have different datatype... '
				    F"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
				)
				raise TypeError(UpperCamelCase							)

		_lowerCamelCase				   =  []

		for value in value_array:
				_lowerCamelCase				   =  euclidean(UpperCamelCase			,       dataset[0]							)
				_lowerCamelCase				   =  dataset[0].tolist()

				for dataset_value in dataset[1:]:
						_lowerCamelCase				   =  euclidean(UpperCamelCase			,       UpperCamelCase							)

						if dist > temp_dist:
								_lowerCamelCase				   =  temp_dist
								_lowerCamelCase				   =  dataset_value.tolist()

				answer.append([vector, dist]							)

		return answer


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    float:
		return np.dot(UpperCamelCase			,       UpperCamelCase							) / (norm(UpperCamelCase							) * norm(UpperCamelCase							))


if __name__ == "__main__":
				import doctest

				doctest.testmod()